Overview

Dataset statistics

Number of variables19
Number of observations199
Missing cells6
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory29.7 KiB
Average record size in memory152.7 B

Variable types

Categorical5
Numeric11
Text2
DateTime1

Alerts

year has constant value "2013"Constant
month has constant value "1"Constant
day has constant value "1"Constant
air_time is highly overall correlated with arr_time and 2 other fieldsHigh correlation
arr_delay is highly overall correlated with dep_delayHigh correlation
arr_time is highly overall correlated with air_time and 5 other fieldsHigh correlation
carrier is highly overall correlated with originHigh correlation
dep_delay is highly overall correlated with arr_delayHigh correlation
dep_time is highly overall correlated with arr_time and 3 other fieldsHigh correlation
distance is highly overall correlated with air_time and 2 other fieldsHigh correlation
hour is highly overall correlated with arr_time and 3 other fieldsHigh correlation
origin is highly overall correlated with carrierHigh correlation
sched_arr_time is highly overall correlated with air_time and 5 other fieldsHigh correlation
sched_dep_time is highly overall correlated with arr_time and 3 other fieldsHigh correlation
arr_delay has 3 (1.5%) missing valuesMissing
air_time has 3 (1.5%) missing valuesMissing
tailnum has unique valuesUnique
dep_delay has 14 (7.0%) zerosZeros
arr_delay has 3 (1.5%) zerosZeros
minute has 37 (18.6%) zerosZeros

Reproduction

Analysis started2025-12-12 08:32:30.556180
Analysis finished2025-12-12 08:32:49.105859
Duration18.55 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

year
Categorical

Constant 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2013
199 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters796
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2013
2nd row2013
3rd row2013
4th row2013
5th row2013

Common Values

ValueCountFrequency (%)
2013199
100.0%

Length

2025-12-12T14:02:49.223353image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-12T14:02:49.376235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2013199
100.0%

Most occurring characters

ValueCountFrequency (%)
2199
25.0%
0199
25.0%
1199
25.0%
3199
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)796
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2199
25.0%
0199
25.0%
1199
25.0%
3199
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)796
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2199
25.0%
0199
25.0%
1199
25.0%
3199
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)796
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2199
25.0%
0199
25.0%
1199
25.0%
3199
25.0%

month
Categorical

Constant 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
1
199 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters199
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1199
100.0%

Length

2025-12-12T14:02:49.554105image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-12T14:02:49.719983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1199
100.0%

Most occurring characters

ValueCountFrequency (%)
1199
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)199
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1199
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)199
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1199
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)199
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1199
100.0%

day
Categorical

Constant 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
1
199 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters199
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1199
100.0%

Length

2025-12-12T14:02:49.873800image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-12T14:02:50.015508image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1199
100.0%

Most occurring characters

ValueCountFrequency (%)
1199
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)199
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1199
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)199
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1199
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)199
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1199
100.0%

dep_time
Real number (ℝ)

High correlation 

Distinct125
Distinct (%)62.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1616.4472
Minimum1455
Maximum1800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-12-12T14:02:50.148041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1455
5-th percentile1501.8
Q11535
median1619
Q31709.5
95-th percentile1751
Maximum1800
Range345
Interquartile range (IQR)174.5

Descriptive statistics

Standard deviation89.046555
Coefficient of variation (CV)0.05508782
Kurtosis-1.1953638
Mean1616.4472
Median Absolute Deviation (MAD)88
Skewness0.080695233
Sum321673
Variance7929.2889
MonotonicityIncreasing
2025-12-12T14:02:50.354281image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16035
 
2.5%
15524
 
2.0%
15274
 
2.0%
17444
 
2.0%
16554
 
2.0%
15393
 
1.5%
15583
 
1.5%
16103
 
1.5%
16083
 
1.5%
15543
 
1.5%
Other values (115)163
81.9%
ValueCountFrequency (%)
14552
1.0%
14562
1.0%
14572
1.0%
14581
0.5%
14592
1.0%
15001
0.5%
15021
0.5%
15052
1.0%
15062
1.0%
15072
1.0%
ValueCountFrequency (%)
18001
 
0.5%
17591
 
0.5%
17583
1.5%
17572
1.0%
17561
 
0.5%
17531
 
0.5%
17512
1.0%
17501
 
0.5%
17451
 
0.5%
17444
2.0%

sched_dep_time
Real number (ℝ)

High correlation 

Distinct78
Distinct (%)39.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1600.6482
Minimum1310
Maximum1800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-12-12T14:02:50.572711image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1310
5-th percentile1452.7
Q11527.5
median1601
Q31700
95-th percentile1745.1
Maximum1800
Range490
Interquartile range (IQR)172.5

Descriptive statistics

Standard deviation100.24307
Coefficient of variation (CV)0.062626547
Kurtosis-0.47457178
Mean1600.6482
Median Absolute Deviation (MAD)83
Skewness-0.16820438
Sum318529
Variance10048.674
MonotonicityNot monotonic
2025-12-12T14:02:50.746622image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
160015
 
7.5%
170012
 
6.0%
153010
 
5.0%
16309
 
4.5%
17458
 
4.0%
15007
 
3.5%
17205
 
2.5%
15455
 
2.5%
17305
 
2.5%
14595
 
2.5%
Other values (68)118
59.3%
ValueCountFrequency (%)
13101
0.5%
13381
0.5%
13401
0.5%
13591
0.5%
14201
0.5%
14301
0.5%
14371
0.5%
14431
0.5%
14451
0.5%
14501
0.5%
ValueCountFrequency (%)
18003
 
1.5%
17592
 
1.0%
17502
 
1.0%
17492
 
1.0%
17461
 
0.5%
17458
4.0%
17401
 
0.5%
17305
2.5%
17292
 
1.0%
17253
 
1.5%

dep_delay
Real number (ℝ)

High correlation  Zeros 

Distinct57
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.577889
Minimum-14
Maximum122
Zeros14
Zeros (%)7.0%
Negative87
Negative (%)43.7%
Memory size1.7 KiB
2025-12-12T14:02:50.928799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-14
5-th percentile-8
Q1-4
median0
Q312
95-th percentile82.2
Maximum122
Range136
Interquartile range (IQR)16

Descriptive statistics

Standard deviation26.865409
Coefficient of variation (CV)2.3204064
Kurtosis4.6838322
Mean11.577889
Median Absolute Deviation (MAD)6
Skewness2.2288365
Sum2304
Variance721.75022
MonotonicityNot monotonic
2025-12-12T14:02:51.127944image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-514
 
7.0%
-314
 
7.0%
014
 
7.0%
-613
 
6.5%
-412
 
6.0%
-212
 
6.0%
18
 
4.0%
-87
 
3.5%
27
 
3.5%
86
 
3.0%
Other values (47)92
46.2%
ValueCountFrequency (%)
-141
 
0.5%
-101
 
0.5%
-92
 
1.0%
-87
3.5%
-75
 
2.5%
-613
6.5%
-514
7.0%
-412
6.0%
-314
7.0%
-212
6.0%
ValueCountFrequency (%)
1221
 
0.5%
1191
 
0.5%
1151
 
0.5%
1051
 
0.5%
911
 
0.5%
884
2.0%
841
 
0.5%
821
 
0.5%
711
 
0.5%
701
 
0.5%

arr_time
Real number (ℝ)

High correlation 

Distinct143
Distinct (%)71.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1879.1759
Minimum1637
Maximum2158
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-12-12T14:02:51.342890image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1637
5-th percentile1655
Q11780
median1858
Q32002
95-th percentile2054.1
Maximum2158
Range521
Interquartile range (IQR)222

Descriptive statistics

Standard deviation127.00093
Coefficient of variation (CV)0.067583313
Kurtosis-0.859473
Mean1879.1759
Median Absolute Deviation (MAD)105
Skewness0.014085162
Sum373956
Variance16129.237
MonotonicityNot monotonic
2025-12-12T14:02:51.542247image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18304
 
2.0%
17533
 
1.5%
20543
 
1.5%
20023
 
1.5%
20203
 
1.5%
19043
 
1.5%
17313
 
1.5%
19123
 
1.5%
19133
 
1.5%
19253
 
1.5%
Other values (133)168
84.4%
ValueCountFrequency (%)
16371
0.5%
16381
0.5%
16431
0.5%
16491
0.5%
16501
0.5%
16512
1.0%
16521
0.5%
16541
0.5%
16552
1.0%
16571
0.5%
ValueCountFrequency (%)
21581
0.5%
21541
0.5%
21401
0.5%
21261
0.5%
21211
0.5%
21091
0.5%
21051
0.5%
21021
0.5%
20581
0.5%
20551
0.5%

sched_arr_time
Real number (ℝ)

High correlation 

Distinct137
Distinct (%)68.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1851.6482
Minimum1431
Maximum2201
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-12-12T14:02:51.718543image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1431
5-th percentile1640.8
Q11743.5
median1840
Q31950
95-th percentile2052.3
Maximum2201
Range770
Interquartile range (IQR)206.5

Descriptive statistics

Standard deviation136.54228
Coefficient of variation (CV)0.07374094
Kurtosis-0.37190773
Mean1851.6482
Median Absolute Deviation (MAD)101
Skewness-0.082560147
Sum368478
Variance18643.795
MonotonicityNot monotonic
2025-12-12T14:02:52.158276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19105
 
2.5%
16554
 
2.0%
19154
 
2.0%
18204
 
2.0%
18104
 
2.0%
18404
 
2.0%
16563
 
1.5%
18553
 
1.5%
17353
 
1.5%
18303
 
1.5%
Other values (127)162
81.4%
ValueCountFrequency (%)
14311
0.5%
15151
0.5%
15531
0.5%
16031
0.5%
16201
0.5%
16261
0.5%
16281
0.5%
16321
0.5%
16341
0.5%
16391
0.5%
ValueCountFrequency (%)
22011
0.5%
21151
0.5%
21121
0.5%
21102
1.0%
21051
0.5%
21001
0.5%
20591
0.5%
20581
0.5%
20551
0.5%
20521
0.5%

arr_delay
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct90
Distinct (%)45.9%
Missing3
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean15.183673
Minimum-40
Maximum136
Zeros3
Zeros (%)1.5%
Negative64
Negative (%)32.2%
Memory size1.7 KiB
2025-12-12T14:02:52.382964image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-40
5-th percentile-23.25
Q1-4
median8
Q325.25
95-th percentile78.5
Maximum136
Range176
Interquartile range (IQR)29.25

Descriptive statistics

Standard deviation32.629968
Coefficient of variation (CV)2.1490167
Kurtosis2.6887416
Mean15.183673
Median Absolute Deviation (MAD)14
Skewness1.4835729
Sum2976
Variance1064.7148
MonotonicityNot monotonic
2025-12-12T14:02:52.590079image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
311
 
5.5%
-47
 
3.5%
167
 
3.5%
66
 
3.0%
45
 
2.5%
174
 
2.0%
-174
 
2.0%
-124
 
2.0%
-64
 
2.0%
-54
 
2.0%
Other values (80)140
70.4%
ValueCountFrequency (%)
-401
0.5%
-351
0.5%
-341
0.5%
-331
0.5%
-321
0.5%
-301
0.5%
-291
0.5%
-281
0.5%
-242
1.0%
-231
0.5%
ValueCountFrequency (%)
1361
0.5%
1271
0.5%
1251
0.5%
1232
1.0%
1151
0.5%
1071
0.5%
961
0.5%
911
0.5%
801
0.5%
781
0.5%

carrier
Categorical

High correlation 

Distinct12
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
UA
41 
B6
36 
DL
29 
EV
27 
AA
21 
Other values (7)
45 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters398
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st rowB6
2nd rowDL
3rd rowUA
4th rowUA
5th rowUA

Common Values

ValueCountFrequency (%)
UA41
20.6%
B636
18.1%
DL29
14.6%
EV27
13.6%
AA21
10.6%
MQ17
8.5%
9E9
 
4.5%
WN8
 
4.0%
US6
 
3.0%
FL2
 
1.0%
Other values (2)3
 
1.5%

Length

2025-12-12T14:02:52.802803image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ua41
20.6%
b636
18.1%
dl29
14.6%
ev27
13.6%
aa21
10.6%
mq17
8.5%
9e9
 
4.5%
wn8
 
4.0%
us6
 
3.0%
fl2
 
1.0%
Other values (2)3
 
1.5%

Most occurring characters

ValueCountFrequency (%)
A83
20.9%
U47
11.8%
B36
9.0%
636
9.0%
E36
9.0%
L31
 
7.8%
D29
 
7.3%
V29
 
7.3%
M17
 
4.3%
Q17
 
4.3%
Other values (6)37
9.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)398
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A83
20.9%
U47
11.8%
B36
9.0%
636
9.0%
E36
9.0%
L31
 
7.8%
D29
 
7.3%
V29
 
7.3%
M17
 
4.3%
Q17
 
4.3%
Other values (6)37
9.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)398
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A83
20.9%
U47
11.8%
B36
9.0%
636
9.0%
E36
9.0%
L31
 
7.8%
D29
 
7.3%
V29
 
7.3%
M17
 
4.3%
Q17
 
4.3%
Other values (6)37
9.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)398
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A83
20.9%
U47
11.8%
B36
9.0%
636
9.0%
E36
9.0%
L31
 
7.8%
D29
 
7.3%
V29
 
7.3%
M17
 
4.3%
Q17
 
4.3%
Other values (6)37
9.3%

flight
Real number (ℝ)

Distinct196
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1805.6935
Minimum4
Maximum5712
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-12-12T14:02:52.983417image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile78.9
Q1511.5
median1222
Q33353.5
95-th percentile4572.7
Maximum5712
Range5708
Interquartile range (IQR)2842

Descriptive statistics

Standard deviation1626.3608
Coefficient of variation (CV)0.90068489
Kurtosis-0.84271026
Mean1805.6935
Median Absolute Deviation (MAD)845
Skewness0.77170784
Sum359333
Variance2645049.5
MonotonicityNot monotonic
2025-12-12T14:02:53.178379image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5092
 
1.0%
16352
 
1.0%
7022
 
1.0%
10531
 
0.5%
3791
 
0.5%
7201
 
0.5%
44291
 
0.5%
56751
 
0.5%
11051
 
0.5%
3771
 
0.5%
Other values (186)186
93.5%
ValueCountFrequency (%)
41
0.5%
81
0.5%
91
0.5%
121
0.5%
151
0.5%
271
0.5%
311
0.5%
351
0.5%
631
0.5%
691
0.5%
ValueCountFrequency (%)
57121
0.5%
57091
0.5%
56751
0.5%
51631
0.5%
47051
0.5%
46611
0.5%
45881
0.5%
45811
0.5%
45801
0.5%
45791
0.5%

tailnum
Text

Unique 

Distinct199
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2025-12-12T14:02:53.608191image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.9949749
Min length5

Characters and Unicode

Total characters1193
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique199 ?
Unique (%)100.0%

Sample

1st rowN203JB
2nd rowN997DL
3rd rowN802UA
4th rowN24212
5th rowN401UA
ValueCountFrequency (%)
n203jb1
 
0.5%
n997dl1
 
0.5%
n802ua1
 
0.5%
n242121
 
0.5%
n401ua1
 
0.5%
n539uw1
 
0.5%
n736mq1
 
0.5%
n155721
 
0.5%
n754351
 
0.5%
n633jb1
 
0.5%
Other values (189)189
95.0%
2025-12-12T14:02:54.201807image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N214
17.9%
188
 
7.4%
386
 
7.2%
A75
 
6.3%
575
 
6.3%
673
 
6.1%
967
 
5.6%
766
 
5.5%
458
 
4.9%
258
 
4.9%
Other values (22)333
27.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)1193
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N214
17.9%
188
 
7.4%
386
 
7.2%
A75
 
6.3%
575
 
6.3%
673
 
6.1%
967
 
5.6%
766
 
5.5%
458
 
4.9%
258
 
4.9%
Other values (22)333
27.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1193
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N214
17.9%
188
 
7.4%
386
 
7.2%
A75
 
6.3%
575
 
6.3%
673
 
6.1%
967
 
5.6%
766
 
5.5%
458
 
4.9%
258
 
4.9%
Other values (22)333
27.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1193
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N214
17.9%
188
 
7.4%
386
 
7.2%
A75
 
6.3%
575
 
6.3%
673
 
6.1%
967
 
5.6%
766
 
5.5%
458
 
4.9%
258
 
4.9%
Other values (22)333
27.9%

origin
Categorical

High correlation 

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
EWR
74 
JFK
73 
LGA
52 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters597
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJFK
2nd rowLGA
3rd rowLGA
4th rowEWR
5th rowEWR

Common Values

ValueCountFrequency (%)
EWR74
37.2%
JFK73
36.7%
LGA52
26.1%

Length

2025-12-12T14:02:54.396976image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-12T14:02:54.554853image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
ewr74
37.2%
jfk73
36.7%
lga52
26.1%

Most occurring characters

ValueCountFrequency (%)
E74
12.4%
W74
12.4%
R74
12.4%
J73
12.2%
F73
12.2%
K73
12.2%
L52
8.7%
G52
8.7%
A52
8.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)597
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E74
12.4%
W74
12.4%
R74
12.4%
J73
12.2%
F73
12.2%
K73
12.2%
L52
8.7%
G52
8.7%
A52
8.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)597
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E74
12.4%
W74
12.4%
R74
12.4%
J73
12.2%
F73
12.2%
K73
12.2%
L52
8.7%
G52
8.7%
A52
8.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)597
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E74
12.4%
W74
12.4%
R74
12.4%
J73
12.2%
F73
12.2%
K73
12.2%
L52
8.7%
G52
8.7%
A52
8.7%

dest
Text

Distinct58
Distinct (%)29.1%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2025-12-12T14:02:54.787421image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters597
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)10.6%

Sample

1st rowPIT
2nd rowPBI
3rd rowORD
4th rowAUS
5th rowRSW
ValueCountFrequency (%)
ord13
 
6.5%
mco13
 
6.5%
atl10
 
5.0%
fll9
 
4.5%
lax8
 
4.0%
den8
 
4.0%
clt7
 
3.5%
tpa6
 
3.0%
dfw6
 
3.0%
sfo6
 
3.0%
Other values (48)113
56.8%
2025-12-12T14:02:55.218401image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A58
 
9.7%
L55
 
9.2%
D51
 
8.5%
S45
 
7.5%
O41
 
6.9%
T37
 
6.2%
C37
 
6.2%
M32
 
5.4%
R30
 
5.0%
I27
 
4.5%
Other values (15)184
30.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)597
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A58
 
9.7%
L55
 
9.2%
D51
 
8.5%
S45
 
7.5%
O41
 
6.9%
T37
 
6.2%
C37
 
6.2%
M32
 
5.4%
R30
 
5.0%
I27
 
4.5%
Other values (15)184
30.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)597
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A58
 
9.7%
L55
 
9.2%
D51
 
8.5%
S45
 
7.5%
O41
 
6.9%
T37
 
6.2%
C37
 
6.2%
M32
 
5.4%
R30
 
5.0%
I27
 
4.5%
Other values (15)184
30.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)597
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A58
 
9.7%
L55
 
9.2%
D51
 
8.5%
S45
 
7.5%
O41
 
6.9%
T37
 
6.2%
C37
 
6.2%
M32
 
5.4%
R30
 
5.0%
I27
 
4.5%
Other values (15)184
30.8%

air_time
Real number (ℝ)

High correlation  Missing 

Distinct128
Distinct (%)65.3%
Missing3
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean168.9898
Minimum35
Maximum370
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-12-12T14:02:55.392631image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile45
Q1107.5
median148
Q3235
95-th percentile349.5
Maximum370
Range335
Interquartile range (IQR)127.5

Descriptive statistics

Standard deviation89.979598
Coefficient of variation (CV)0.53245581
Kurtosis-0.28735991
Mean168.9898
Median Absolute Deviation (MAD)45
Skewness0.73444288
Sum33122
Variance8096.3281
MonotonicityNot monotonic
2025-12-12T14:02:55.593464image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1525
 
2.5%
1565
 
2.5%
1464
 
2.0%
1414
 
2.0%
1713
 
1.5%
1673
 
1.5%
1353
 
1.5%
1403
 
1.5%
2473
 
1.5%
1063
 
1.5%
Other values (118)160
80.4%
ValueCountFrequency (%)
352
1.0%
362
1.0%
381
0.5%
401
0.5%
412
1.0%
421
0.5%
452
1.0%
482
1.0%
521
0.5%
532
1.0%
ValueCountFrequency (%)
3701
0.5%
3631
0.5%
3621
0.5%
3602
1.0%
3572
1.0%
3561
0.5%
3541
0.5%
3511
0.5%
3491
0.5%
3481
0.5%

distance
Real number (ℝ)

High correlation 

Distinct103
Distinct (%)51.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1050.0251
Minimum94
Maximum2586
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-12-12T14:02:55.794240image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum94
5-th percentile200
Q1544
median944
Q31395.5
95-th percentile2475
Maximum2586
Range2492
Interquartile range (IQR)851.5

Descriptive statistics

Standard deviation672.04929
Coefficient of variation (CV)0.64003163
Kurtosis-0.033468908
Mean1050.0251
Median Absolute Deviation (MAD)428
Skewness0.89158702
Sum208955
Variance451650.25
MonotonicityNot monotonic
2025-12-12T14:02:56.002694image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24756
 
3.0%
7626
 
3.0%
9376
 
3.0%
7195
 
2.5%
7335
 
2.5%
10694
 
2.0%
16204
 
2.0%
9444
 
2.0%
25864
 
2.0%
15984
 
2.0%
Other values (93)151
75.9%
ValueCountFrequency (%)
941
 
0.5%
1431
 
0.5%
1841
 
0.5%
1873
1.5%
1993
1.5%
2002
1.0%
2092
1.0%
2121
 
0.5%
2131
 
0.5%
2282
1.0%
ValueCountFrequency (%)
25864
2.0%
25652
 
1.0%
24756
3.0%
24543
1.5%
24463
1.5%
24251
 
0.5%
24021
 
0.5%
22482
 
1.0%
22271
 
0.5%
21532
 
1.0%

hour
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.748744
Minimum13
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-12-12T14:02:56.184553image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile14
Q115
median16
Q317
95-th percentile17
Maximum18
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.0479349
Coefficient of variation (CV)0.066540858
Kurtosis-0.40593226
Mean15.748744
Median Absolute Deviation (MAD)1
Skewness-0.27905371
Sum3134
Variance1.0981676
MonotonicityNot monotonic
2025-12-12T14:02:56.343803image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1562
31.2%
1661
30.7%
1752
26.1%
1417
 
8.5%
134
 
2.0%
183
 
1.5%
ValueCountFrequency (%)
134
 
2.0%
1417
 
8.5%
1562
31.2%
1661
30.7%
1752
26.1%
183
 
1.5%
ValueCountFrequency (%)
183
 
1.5%
1752
26.1%
1661
30.7%
1562
31.2%
1417
 
8.5%
134
 
2.0%

minute
Real number (ℝ)

Zeros 

Distinct41
Distinct (%)20.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.773869
Minimum0
Maximum59
Zeros37
Zeros (%)18.6%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2025-12-12T14:02:56.497557image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19
median29
Q344.5
95-th percentile57
Maximum59
Range59
Interquartile range (IQR)35.5

Descriptive statistics

Standard deviation19.191594
Coefficient of variation (CV)0.7446144
Kurtosis-1.2725466
Mean25.773869
Median Absolute Deviation (MAD)16
Skewness0.078756422
Sum5129
Variance368.31729
MonotonicityNot monotonic
2025-12-12T14:02:56.692280image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
037
18.6%
3025
12.6%
4514
 
7.0%
5011
 
5.5%
2011
 
5.5%
1010
 
5.0%
599
 
4.5%
159
 
4.5%
57
 
3.5%
407
 
3.5%
Other values (31)59
29.6%
ValueCountFrequency (%)
037
18.6%
13
 
1.5%
21
 
0.5%
31
 
0.5%
57
 
3.5%
92
 
1.0%
1010
 
5.0%
121
 
0.5%
159
 
4.5%
162
 
1.0%
ValueCountFrequency (%)
599
4.5%
572
 
1.0%
555
2.5%
541
 
0.5%
531
 
0.5%
5011
5.5%
492
 
1.0%
483
 
1.5%
471
 
0.5%
461
 
0.5%
Distinct6
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
Minimum2013-01-01 13:00:00
Maximum2013-01-01 18:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-12-12T14:02:56.860482image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:57.009582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)

Interactions

2025-12-12T14:02:46.938463image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:31.326883image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:33.133924image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:34.615428image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:36.165744image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:37.598858image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:39.472065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:40.965763image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:42.233476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:43.715071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:45.258208image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:47.078884image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:31.496569image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:33.296549image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:34.755795image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:36.291609image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:37.760248image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:39.620744image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:41.089382image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:42.382556image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:43.847819image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:45.426226image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:47.216861image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:31.663706image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:33.410996image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:34.897003image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:36.423164image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:37.923864image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:39.727818image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:41.188861image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:42.524177image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:43.989853image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:45.553810image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:47.368029image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:31.839286image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:33.544834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:35.040878image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:36.550815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:38.081844image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:39.865025image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:41.299056image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:42.682402image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:44.128984image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:45.691147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:47.469690image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:31.976212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:33.677324image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:35.174439image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:36.665353image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:38.490167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:39.996788image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:41.397420image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:42.789256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:44.249674image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:45.827906image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:47.619406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:32.187190image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:33.824340image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:35.335356image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:36.826618image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:38.657577image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:40.132343image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:41.511273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:42.939738image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:44.384249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:46.175880image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:47.737945image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:32.346232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:33.965474image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:35.473373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:36.955855image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:38.793688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:40.288668image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:41.621782image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:43.062878image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:44.527040image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:46.293420image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:47.872471image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:32.509403image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:34.094105image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:35.610735image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:37.082317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:38.927852image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:40.428147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:41.731288image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:43.202698image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:44.652719image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:46.436152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:48.003054image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:32.682251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:34.246256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:35.758678image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:37.203902image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:39.057898image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:40.586592image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:41.847188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:43.325444image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:44.793241image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:46.569248image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:48.128465image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:32.827105image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:34.377747image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:35.910377image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:37.327673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:39.187440image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:40.707699image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:41.982864image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:43.474482image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:44.910822image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:46.688849image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:48.263965image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:32.980416image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:34.494955image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:36.025249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:37.463000image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:39.321555image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:40.827454image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:42.095412image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:43.582966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:45.150692image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-12T14:02:46.805669image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-12-12T14:02:57.144106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
air_timearr_delayarr_timecarrierdep_delaydep_timedistanceflighthourminuteoriginsched_arr_timesched_dep_time
air_time1.000-0.1060.5610.264-0.0430.0640.973-0.4560.118-0.0350.2290.5640.112
arr_delay-0.1061.0000.0220.1080.7010.037-0.1860.219-0.2790.1900.291-0.310-0.248
arr_time0.5610.0221.0000.0000.0620.7550.595-0.3070.6600.0640.0000.9070.703
carrier0.2640.1080.0001.0000.1130.0000.2860.4020.0610.0000.5530.1160.000
dep_delay-0.0430.7010.0620.1131.0000.089-0.0700.095-0.2950.2240.284-0.202-0.251
dep_time0.0640.0370.7550.0000.0891.0000.081-0.1020.8530.0580.0000.7120.910
distance0.973-0.1860.5950.286-0.0700.0811.000-0.4830.139-0.0380.2420.6200.132
flight-0.4560.219-0.3070.4020.095-0.102-0.4831.000-0.137-0.0440.246-0.406-0.147
hour0.118-0.2790.6600.061-0.2950.8530.139-0.1371.000-0.3050.0620.7540.961
minute-0.0350.1900.0640.0000.2240.058-0.038-0.044-0.3051.0000.000-0.022-0.051
origin0.2290.2910.0000.5530.2840.0000.2420.2460.0620.0001.0000.1160.036
sched_arr_time0.564-0.3100.9070.116-0.2020.7120.620-0.4060.754-0.0220.1161.0000.786
sched_dep_time0.112-0.2480.7030.000-0.2510.9100.132-0.1470.961-0.0510.0360.7861.000

Missing values

2025-12-12T14:02:48.478474image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-12T14:02:48.815529image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-12-12T14:02:49.026341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

yearmonthdaydep_timesched_dep_timedep_delayarr_timesched_arr_timearr_delaycarrierflighttailnumorigindestair_timedistancehourminutetime_hour
020131114551459-41655164510.0B61053N203JBJFKPIT87.0340145901-01-2013 14:00
120131114551500-517531810-17.0DL1997N997DLLGAPBI152.0103515001-01-2013 15:00
220131114561500-41649163217.0UA685N802UALGAORD140.073315001-01-2013 15:00
32013111456145511830181317.0UA1134N24212EWRAUS252.01504145501-01-2013 14:00
420131114571500-317581815-17.0UA379N401UAEWRRSW166.0106815001-01-2013 15:00
520131114571500-316521656-4.0US720N539UWEWRCLT97.052915001-01-2013 15:00
620131114581500-2165816553.0MQ4429N736MQLGACMH94.047915001-01-2013 15:00
720131114591501-2165116510.0EV5675N15572EWRCMH96.046315101-01-2013 15:00
820131114591454517501751-1.0UA1105N75435EWRTPA152.0997145401-01-2013 14:00
9201311150014591180918063.0B6377N633JBLGAFLL167.01076145901-01-2013 14:00
yearmonthdaydep_timesched_dep_timedep_delayarr_timesched_arr_timearr_delaycarrierflighttailnumorigindestair_timedistancehourminutetime_hour
1892013111751174562015191065.0WN3384N764SWEWRMDW148.0711174501-01-2013 17:00
1902013111753174582058203721.0B6391N630JBLGAMCO144.0950174501-01-2013 17:00
19120131117561725312036201917.0UA376N523UAEWRMCO140.0937172501-01-2013 17:00
19220131117571703541904181351.0EV4373N14998EWRDCA45.019917301-01-2013 17:00
19320131117571759-220272042-15.0DL1047N643DLLGAATL125.0762175901-01-2013 17:00
19420131117581800-219051917-12.0B61016N304JBJFKBOS36.018718001-01-2013 18:00
19520131117581800-221052110-5.0B6989N663JBJFKFLL152.0106918001-01-2013 18:00
1962013111758174992020194337.0UA1676N37274EWRORD135.0719174901-01-2013 17:00
197201311175917590195719498.0EV4581N13566EWRCMH95.0463175901-01-2013 17:00
19820131118001800019451951-6.0B61111N294JBJFKRDU78.042718001-01-2013 18:00